Effects of airway smooth muscle contraction and inflammation on lung tissue compliance
Bibliographic record
Abstract
There are renewed interests in using the parameter K of Salazar–Knowles’ equation to assess lung tissue compliance. K either decreases or increases when the lung’s parenchyma stiffens or loosens, respectively. However, whether K is affected by other common features of respiratory diseases, such as inflammation and airway smooth muscle (ASM) contraction, is unknown. Herein, male C57BL/6 mice were treated intranasally with either saline or lipopolysaccharide (LPS) at 1 mg/kg to induce pulmonary inflammation. They were then subjected to either a multiple or a single-dose challenge with methacholine to activate ASM to different degrees. A quasi-static pressure-driven partial pressure-volume (P-V) maneuver was performed before and after methacholine. The Salazar–Knowles’ equation was then fitted to the deflation limb of the P-V loop to obtain K, as well as the parameter A, an estimate of lung volume (inspiratory capacity). The fitted curve was also used to derive the quasi-static elastance (Est) at 5 cmH2O. The results demonstrate that LPS and both methacholine challenges increased Est. LPS also decreased A, but did not affect K. In contradistinction, methacholine decreased both A and K in the multiple-dose challenge, whereas it decreased K but not A in the single-dose challenge. These results suggest that LPS increases Est by reducing the open lung volume (A) and without affecting tissue compliance (K), whereas methacholine increases Est by decreasing tissue compliance with or without affecting lung volume. We conclude that lung tissue compliance, assessed using the parameter K of Salazar–Knowles’ equation, is insensitive to inflammation but sensitive to ASM contraction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".